In this thesis, a method for aerial image segmentation has been proposed to select an appropriate site for unmanned aerial vehicle (UAV) emergency landing. Up to now the design and development of the UAVs have been mostly for military requirements. Technology development, UAV benefits and large number of potential UAV applications in civilian sector such as commercial, security, and scientific leading to the UAVs entrance into residential areas in the near future. To gain public acceptance and to develop UAV use in the civilian branches, concerns about the safety issues should be resolved and the UAV should be able to react in a similar manner to pilots in emergency situatio including that UAV must be capable of finding a suitable landing location automatically. The objective of this research is Landing Site Selection for UAV Forced Using Machine Vision. For this, different parts of aerial images should be segmented and There are two main categories that can be used to achieve this outcome and they are called Supervised and Unsupervised left; MARGIN: 0cm 0cm 0pt; unicode-bidi: embed; DIRECTION: ltr" align=left Keywords: Automatic Landing Site Selection, Aerial Image Supervised Segmentation,Pixel Classification, Color-Texture Segmentation, Feature Distribution, LBP, Color Histogram, K-NN Classifier, Semantic Segmentation
